Definition
A Neural Network Model, also known as an artificial neural network (ANN), is a type of machine learning model inspired by the structure and function of the human brain.
While this model is applied in the Marketing domain, it becomes the Marketing Neural Networking Model.
Instead of diving into the intricacy of the mathematical formula and operation, we instead will put the spotlight on the semantic logic behind the calculation.
What Problem Pattern the Marketing Nerual Networking Model Solves
Formulate Markeitng Strategy via A.I.
In a nutshell, while Marketing Consultant is mainly to providing Marketing Strategy, a Marketing Strategy is simply making a series of decisions on how to choose among alternatives. For example, if you want to sell a Tattoo Printer to teenages, will you use Facebook or Instagram to promote your product?
To choose between “Facebook” or “Instagram” (i.e. 2 alternatives) is called Marketing Strategy. For sure, in reality, it always takes more than 1 factor (or attribute) to make a decision, and takes more than 1 decisions to formuate a strategy . You can imagine it’s in fact a dynamic decision chain in which the outcome of 1 decision will affect not only the outcome, but also even the option values (i.e. all alternatives) of the decision.
The Marketing Neural Networking Model is purposed to learn and solve how to make decisions in a scientific way.
Only after we turn the decision making process in a scientific way can we automate the decision making process via A.I. by applying the Marketing Neural Networking Model, which in turn creates an A.I. Marketing Consultant.
How Marketing Neural Networking Model look likes
Although the intricacy of the Neural Networking Model is a bit scary, decoupling it in piecemeal and demonstrating with a story, will definitely aid you to comprehend the concept more efficiently. Bear in mind that it is obviously a simplified example which in reality will be 1000 times in scale.
Before starting the story, allow us to provide you the legend of the Figure (Marketing Neural Networking Model) above:
Rectangle ( ▭ ) : The Attribute (or Property, or Layer) of the Object, which the Object is the Marketing Neural Network Model.
Circle (○) : Nodes (i.e. any Business Concepts)
Sold Line ( ⎯⎯ ) : Positive Edges which has directionaly relationship between 2 Nodes
Dot Line (···) : Negative Edges which has NO directional relationship between 2 Nodes
Imagine you are the CEO of a conglomerate which at the same time run a Fashion Retail Store as well as a Diamond Wholesaler business. You are required by your shareholders to incrementally increase the ROI of the conglomerate by 10X, which is quite an impossible mission. In order to achieve this goal, you start by enumerating all the “Concepts” (i.e. the Node) in your mind which related to the business as below:
- Fashion Retail Store
- Diamond Wholesaler
- Website
- Google Merchant Center
- Linkedin Business Page
- Ads
- Payment Gateway
- Feed
- Enquiry Form
In reality, the process of addressing , enumerating and filtering all the Concepts (i.e. the Nodes) relating to the business is almost an impossible task for human beings. The more knowledge Nodes the marketer acquired and manipulated, the more professional he is.
Back to our story, immedate after you enumerated all the Nodes in your mind which you think are related to your business, you addressed some pattern that there are some patterns within these Nodes:
Causal Relationship
Having played around with the interface of the Google Merchant Center for a day, you realized that Google Merchant Center is mainly designed for listing the products in the storefront of Google Shopping Tab in retail price, and therefore the Google Merchant Center is better to apply in any retail instead of wholesale business because there is no any field for the Google Merchant Center to insert any tiered pricing or bulk discount in the storefront. In this sense, you addressed that what Digital Assets (Attribute) you are uisng will be dependent to the Business Model (Attribute 1). Therefore you deduce your own business rule (which is called business intelligent in the business world) as below:
Business Rules 1 : Digital Assets is dependent to the Business Model
By applying Business Rules 1 in your business, you decide to adapt the Google Merchant Center into your Fashion Retail Store (Edge 2) and meanwhile NOT adapt in your Diamond Wholesaler business (Edge 5)
Correlation Coefficient
While having 10 years experience on using Linkedin Business Page, you understand that the users who are responsive in Linkedin are mainly seeking for business opportunities (i.e. B2B) instead of retail purchasing (i.e. B2C). Although you have this “insight”, you still from time to time scrolled to some Feeds in Linkedin which are selling to retail customers. As you cannot 100% sure about your insight, and therefore you classify it as a Correlation Coefficient (denotes “r”) relationship which the Correlation Coefficient of the responsiveness between Linkedin Business Page and Retail Business is low (e.g. r=0.3) , and meanwhile it is high (e.g. r=0.9) between Linked Business Page and Wholesale business.
In this stage, you can bypass the understanding of the mathematical operation of the Correlation Coefficient. What you need to know is simply that the higher the value of the Correlation Coefficient (r) , the closer the relationship to (Positive) Causal Relationship.
Now based on the Correlation Coefficient which is conducted by your empirical study, you deduce other Business Rule as below:
Business Rule 2 : The responsiveness of the Linkedin Business Page is high for Wholesale Business and low for Retail Business.
By applying Business Rules 2 in your business, you decide to adapt the Linkedin Business Page into your Diamond Wholesaler Store (Edge 6) and meanwhile NOT adapt in your Fashion Retail Store business (Edge 3)
By continuing deducing the Business Rules based on your experience or any other statistic, you figured out the following Business Rules for the Edge as below:
| Decision# | Involved Edge | Business Rules |
|---|---|---|
| Edge #1 and #7 | Fashion Retail Store > Website > Ads | Fashion Retail Store needs Website as the landing page of placing Ads. |
| Edge #1 and #8 | Fashion Retail Store > Website > Payment Gateway | Fashion Retail Store needs Payment Gateway to install in Website to receive payment from Client |
| Edge #1 and #9 | Fashion Retail Store > Website > Feed | Fashion Retail Store needs put the Feed in the Website for content marketing articles publishing |
| Edge #1 and #10 | Fashion Retail Store > Website > Enquiry Form | Fashion Retail Store needs put the Enquiry Form in the Website for replying questions from client. |
| Edge #2 and #11 | Fashion Retail Store > Google Merchant Center > Ads | Fashion Retail Store needs Google Merchant Center showcasing their product in Google Ads Campaign |
| Edge #2 and #12 | Fashion Retail Store > Google Merchant Center > Payment Gateway | Google Merchant Center does not support Payment Gateway |
| Edge #2 and #13 | Fashion Retail Store > Google Merchant Center > Feed | Fashion Retail Store needs to turn the Product Page of the website to Google Merchant Center’s Feed |
| Edge #2 and #14 | Fashion Retail Store > Google Merchant Center > Enquiry Form | Fashion Retail Store does not support Enquiry Form Function |
| Edge #3 and #15 | Fashion Retail Store > Linkedin Business Page > Ads | Ads placed in Linkedin Business Page is not appropriate for Fashion Retail Store |
| Edge #3 and #16 | Fashion Retail Store > Linkedin Business Page > Payment Gateway | Linkedin Business Page does not support Payment Gateway |
| Edge #3 and #17 | Fashion Retail Store > Linkedin Business Page > Feed | Audience of Linkedin Business Page is not expected Retail Feed from Fashion Retail Store showing in their Linkedin Personal account. |
| Edge #3 and #18 | Fashion Retail Store > Linkedin Business Page > Enquiry Form | There is no Enquiry Form function in Linkedin Business Page |
| Edge #4 and #7 | Diamond Wholesaler > Website > Ads | Diamond Wholesaler needs Website as the landing page of placing Ads. |
| Edge #4 and #8 | Diamond Wholesaler > Website > Payment Gateway | Diamond Wholesaler does not expect the client to place order in the Website directly. Therefore Payment Gateway is not needed. |
| Edge #4 and #9 | Diamond Wholesaler > Website > Feed | Diamond Wholesaler needs put the Feed in the Website for content marketing articles publishing |
| Edge #4 and #10 | Diamond Wholesaler > Website > Enquiry Form | Diamond Wholesaler definitely needs Enquiry Form in the Website as the client will ask for product info and transactional info before placing order. |
| Edge #5 and #11 | Diamond Wholesaler > Google Merchant Center > Ads | Diamond Wholesaler may not need to place the Ads via Google Merchant Center Campaign because Google Merchant Center do not support tiered-pricing or quantity pricing function. |
| Edge #5 and #12 | Diamond Wholesaler > Google Merchant Center > Payment Gateway | Google Merchant Center does not support Payment Gateway |
| Edge #5 and #13 | Diamond Wholesaler > Google Merchant Center > Feed | Diamond Wholesaler may not need to sync the Product Feed from their website to Google Merchant Center because Google Merchant Center do not support tiered-pricing or quantity pricing function. |
| Edge #5 and #14 | Diamond Wholesaler > Google Merchant Center > Enquiry Form | There is no Enquiry Form function in Google Merchant Center. |
| Edge #6 and #15 | Diamond Wholesaler > Linkedin Business Page > Ads | Diamond Wholesaler is appropriate to place Ads in Linkedin Business Page to seek for the management level Decision Maker or Merchandiser based on the Job Title Ads segmentation. |
| Edge #6 and #16 | Diamond Wholesaler > Linkedin Business Page > Payment Gateway | Linkedin Business Page does not support Payment Gateway |
| Edge #6 and #17 | Diamond Wholesaler > Linkedin Business Page > Feed | Diamond Wholesaler is appropriate to publish Feed in Linkedin Business Page to seek for the management level Decision Maker or Merchandiser. |
| Edge #6 and #18 | Diamond Wholesaler > Linkedin Business Page > Enquiry Form | There is no Enquiry Form function in Linkedin Business Page |
Points to note
- Although there are only 18 Edges inside the Model, there are in fact 24 Decision Combinations that we need to make because all of the times we need to put all 3 Attributes (i.e. Business Model / Digital Assets / Digital Assets Features) together into consideration, instead of only considering 2 Attributes each time.
- (Business Model) 2 x (Digital Assets) 3 x (Digital Assets Feature) 4 = 24 Decisions. We call the product ⓘ of the multiplication Carterisan Productⓘ.
What Problem Patterns the Marketing Neural Networking Model Solves
Enumerating all Possible Decisions Combinations
The reason why we need to enumerate all the possbile decision combinations is that while Strategy means “decision“, to formulate a Marketing Strategy, covering all possible decisions comprehensively is as important as figuring out the appropriate answer of a single decision.
The only way to enumerate 100% of the decision combinations is by enumerating all the Attributes and all Option Values of each Attributes, and multiplying them all together to become an Cartersian Product. In turn, there will be no decision combination missing out within the Model (i.e. figured out exactly ALL possibilties within the Model, no more and no less) , provided that there are no relevant attributes in the Marketing Neural Networking Model that are missing out, which we will discuss this “bug” in upcoming chapter.
Automating the Decison Making procedures by computer or A.I.
Remember in the old days (or even today without A.I) you learn digital marketing strategies by listening from the advice provided by the senior digital marketing consultant to the client. Every time when you were participating in a client meeting, you were impressed by how deep the knowledge ocean that the senior digital marketing consultant acquired that seemed he could non stop sharing his knowledge forever. You dropped down every single piece of know-how into a notebook and dreamed of that you might become him some day when you acquired ALL his knowledge, although you never know how “exact quantity” of “ALL” knowledge is.
Even if luckily , you did the miracle and learned “all” the knowledge and become another iconic senior digital marketing consultant, your next generation will encounter the same problem as you did, which he/she needs to take notes and learn piece by piece starting from a blank paper.
This inefficient resistant makes the knowledge transmission process extremely slow, just like what human beings did in the passed 7,000 years since mankind’s history.
Bear in mind that the example that we made previously in this session only describes 24 decision combinations , which accounts for a extremely tiny portion of reality which probably has 10 of millions of decision combinations, which is far beyond the processing power of a mortal within his lifespan.
In order to have a systematic way to record all the Knowledge Nodes and the relationships amongst the Nodes, the Neural Networking Model is a perfect candidate to provide a paradigm which turn reality into a conceptualised mathematical model to do the job , not only by human beings but also by computer, which it’s compute power can dramatically speed up the pace of learning by decade of years, and letting processing ALL decision combinations to be an mission possible.
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